Combining Probability Distributions from Dependent Information Sources
提出一种共识模型，用于合并来自专家或模型等依赖信息源的概率分布，在正态假设下结果可处理，且共识分布对依赖程度敏感。
Inferences or decisions in the face of uncertainty should be based on all available information. Thus, when probability distributions for an uncertain quantity are obtained from experts, models, or other information sources, these distributions should be combined to form a single consensus distribution upon which inferences and decisions can be based. An important feature of information from different sources is the possibility of stochastic dependence, and a consensus model which formally allows for such dependence is developed in this paper. Under normality, the model yields reasonably tractable results, and the consensus distribution is quite sensitive to the degree of dependence.